Recent trends in artificial intelligence and machine learning (AI/ML), dynamic data driven application systems (DDDAS), and cloud computing provide opportunities for enhancing multidomain systems performance. The DDDAS framework utilizes models, measurements, and computation to enhance real-time sensing, performance, and analysis. One example the represents a multi-domain scenario is “fly-by-feel” avionics systems that can support autonomous operations. A "fly-by-feel" system measures the aerodynamic forces (wind, pressure, temperature) for physics-based adaptive flight control to increase maneuverability, safety and fuel efficiency. This paper presents a multidomain approach that identifies safe flight operation platform position needs from which models, data, and information are invoked for effective multidomain control. Concepts are presented to demonstrate the DDDAS approach for enhanced multi-domain coordination bringing together modeling (data at rest), control (data in motion) and command (data in use).
This work presents a novel scalable and field-deployable framework for monitoring lithium-ion (Li-ion) battery state of charge (SoC) and state of health (SoH), based on ultrasonic guided waves using low-profile built-in piezoelectric transducers. The feasibility of this technique is demonstrated through experiments using surface-mounted piezoelectric disc transducers on commercial Li-ion pouch batteries. Pitch-catch guided-wave propagation is performed in synchronization with electrical charge and discharge cycling, and cycle life testing. Simple time-domain analysis shows strong and repeatable correlation between waveform signal parameters, and battery SoC and SoH. The correlation thus provides a building block for constructing a technique for accurate real-time monitoring of battery charge and health states using ultrasonic guided-wave signals. Moreover, capacity-differential signal analysis reveals the underlying physical changes associated with cyclic electrochemical activities and phase transitioning. This finding allows accurate pinpointing of the root cause of capacity fade and mechanical degradation. The results of this study indicate that the use of guided waves can potentially offer a new avenue for in-situ characterization of Li-ion batteries, providing insight on the complex coupling between electrochemistry and mechanics, heretofore not fully understood within the scientific community.
Bondline integrity is still one of the most critical concerns in the design of aircraft structures up to date. Due to the lack of confidence on the integrity of the bondline both during fabrication and service, the industry standards and regulations still require assembling the composite using conventional fasteners. Furthermore, current state-of-the-art non-destructive evaluation (NDE) and structural health monitoring (SHM) techniques are incapable of offering mature solutions on the issue of bondline integrity monitoring. Therefore, the objective of this work is the development of an intelligent adhesive film with integrated micro-sensors for monitoring the integrity of the bondline interface. The proposed method makes use of an electromechanical-impedance (EMI) based method, which is a rapidly evolving approach within the SHM family. Furthermore, an innovative screen-printing technique to fabricate piezoelectric ceramic sensors with minimal thickness has been developed at Stanford. The approach presented in this study is based on the use of (i) micro screen-printed piezoelectric sensors integrated into adhesive leaving a minimal footprint on the material, (ii) numerical and analytical modeling of the EMI spectrum of the adhesive bondline, (iii) novel diagnostic algorithms for monitoring the bondline integrity based on advanced signal processing techniques, and (iv) the experimental assessment via prototype adhesively bonded structures in static (varying loads) and dynamic (fatigue) environments. The proposed method will provide a huge confidence on the use of bonded joints for aerospace structures and lead to a paradigm change in their design by enabling enormous weight savings while maximizing the economic and performance efficiency.
In this work, the system design, integration, and wind tunnel experimental evaluation are presented for a bioinspired self-sensing intelligent composite unmanned aerial vehicle (UAV) wing. A total of 148 micro-sensors, including piezoelectric, strain, and temperature sensors, in the form of stretchable sensor networks are embedded in the layup of a composite wing in order to enable its self-sensing capabilities. Novel stochastic system identification techniques based on time series models and statistical parameter estimation are employed in order to accurately interpret the sensing data and extract real-time information on the coupled air flow-structural dynamics. Special emphasis is given to the wind tunnel experimental assessment under various flight conditions defined by multiple airspeeds and angles of attack. A novel modeling approach based on the recently introduced Vector-dependent Functionally Pooled (VFP) model structure is employed for the stochastic identification of the "global" coupled airflow-structural dynamics of the wing and their correlation with dynamic utter and stall. The obtained results demonstrate the successful system-level integration and effectiveness of the stochastic identification approach, thus opening new perspectives for the state sensing and awareness capabilities of the next generation of "fly-by-fee" UAVs.